PM606 Project

Hypothesis

Postnatal environmental exposures, particularly those found in household products and dietary intake, along with specific serum metabolomics profiles, are significantly associated with the BMI Z-score of children aged 6-11 years. Higher concentrations of certain metabolites in serum, reflecting exposure to chemical classes or metals, will correlate with variations in BMI Z-score, controlling for age and other relevant covariates. High-dimensional metabolomics data can reveal comprehensive biochemical profiles that reflect environmental exposures and metabolic states. Not only that, but some metabolites associated with chemical exposures and dietary patterns can serve as biomarkers for the risk of developing obesity.

Background

Research indicates that postnatal exposure to endocrine-disrupting chemicals (EDCs) such as phthalates, bisphenol A (BPA), and polychlorinated biphenyls (PCBs) can significantly influence body weight and metabolic health (Junge et al., 2018). These chemicals, commonly found in household products and absorbed through dietary intake, are linked to detrimental effects on body weight and metabolic health in children. This hormonal interference can lead to an increased body mass index (BMI) in children, suggesting a potential pathway through which exposure to these chemicals contributes to the development of obesity.

A longitudinal study on Japanese children examined the impact of postnatal exposure (first two years of life) to p,p’-dichlorodiphenyltrichloroethane (p,p’-DDT) and p,p’-dichlorodiphenyldichloroethylene (p,p’-DDE) through breastfeeding (Plouffe et al., 2020). The findings revealed that higher levels of these chemicals in breast milk were associated with increased BMI at 42 months of age. DDT and DDE may interfere with hormonal pathways related to growth and development. These chemicals can mimic or disrupt hormones that regulate metabolism and fat accumulation. This study highlights the importance of understanding how persistent organic pollutants can affect early childhood growth and development.

The study by Harley et al. (2013) investigates the association between prenatal and postnatal Bisphenol A (BPA) exposure and various body composition metrics in children aged 9 years from the CHAMACOS cohort. The study found that higher prenatal BPA exposure was linked to a decrease in BMI and body fat percentages in girls but not boys, suggesting sex-specific effects. Conversely, BPA levels measured at age 9 were positively associated with increased adiposity in both genders, highlighting the different impacts of exposure timing on childhood development.

The 2022 study 2022 study by Uldbjerg et al. explored the effects of combined exposures to multiple EDCs, suggesting that mixtures of these chemicals can have additive or synergistic effects on BMI and obesity risk. Humans are typically exposed to a mixture of chemicals rather than individual EDCs, making it crucial to understand how these mixtures might interact. The research highlighted that the interaction between different EDCs can lead to additive (where the effects simply add up) or even synergistic (where the combined effect is greater than the sum of their separate effects) outcomes. These interactions can significantly amplify the risk factors associated with obesity and metabolic disorders in children. The dose-response relationship found that even low-level exposure to multiple EDCs could result in significant health impacts due to their combined effects.

These studies collectively illustrate the critical role of environmental EDCs in shaping metabolic health outcomes in children, highlighting the necessity for ongoing research and policy intervention to mitigate these risks.

Data Description

This study will utilize data from the subcohort of 1301 mother-child pairs in the HELIX study, who are which aged 6-11 years for whom complete exposure and outcome data were available. Exposure data included detailed dietary records after pregnancy and concentrations of various chemicals like BPA and PCBs in child blood samples. There are categorical and numerical variables, which will include both demographic details and biochemical measurements. This dataset allows for robust statistical analysis to identify potential associations between EDC exposure and changes in BMI Z-scores, considering confounding factors such as age, gender, and socioeconomic status. There are no missing data so there is not need to impute the information. Child BMI Z-scores were calculated based on WHO growth standards.

load(paste0(work.dir, "/HELIX.RData"))
filtered_codebook <- codebook %>%
  filter(domain %in% c("Chemicals", "Lifestyles") & period == "Postnatal" & subfamily != "Allergens")
kable(filtered_codebook, align = "c", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = F)

	variable_name	domain	family	subfamily	period	location	period_postnatal	description	var_type	transformation	labels	labelsshort
h_bfdur_Ter	h_bfdur_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Breastfeeding duration (weeks)	factor	Tertiles	Breastfeeding	Breastfeeding
hs_bakery_prod_Ter	hs_bakery_prod_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Food group: bakery products (hs_cookies + hs_pastries)	factor	Tertiles	Bakery prod	BakeProd
hs_beverages_Ter	hs_beverages_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Food group: beverages (hs_dietsoda+hs_soda)	factor	Tertiles	Soda	Soda
hs_break_cer_Ter	hs_break_cer_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Food group: breakfast cereal (hs_sugarcer+hs_othcer)	factor	Tertiles	BF cereals	BFcereals
hs_caff_drink_Ter	hs_caff_drink_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Drinks a caffeinated or æenergy drink (eg coca-cola, diet-coke, redbull)	factor	Tertiles	Caffeine	Caffeine
hs_dairy_Ter	hs_dairy_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Food group: dairy (hs_cheese + hs_milk + hs_yogurt+ hs_probiotic+ hs_desert)	factor	Tertiles	Dairy	Dairy
hs_fastfood_Ter	hs_fastfood_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Visits a fast food restaurant/take away	factor	Tertiles	Fastfood	Fastfood
hs_KIDMED_None	hs_KIDMED_None	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Sum of KIDMED indices, without index9	numeric	None	KIDMED	KIDMED
hs_mvpa_prd_alt_None	hs_mvpa_prd_alt_None	Lifestyles	Lifestyle	Physical activity	Postnatal	NA	NA	Clean & Over-reporting of Moderate-to-Vigorous Physical Activity (min/day)	numeric	None	PA	PA
hs_org_food_Ter	hs_org_food_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Eats organic food	factor	Tertiles	Organicfood	Organicfood
hs_proc_meat_Ter	hs_proc_meat_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Food group: processed meat (hs_coldmeat+hs_ham)	factor	Tertiles	Processed meat	ProcMeat
hs_readymade_Ter	hs_readymade_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Eats a æready-made supermarket meal	factor	Tertiles	Ready made food	ReadyFood
hs_sd_wk_None	hs_sd_wk_None	Lifestyles	Lifestyle	Physical activity	Postnatal	NA	NA	sedentary behaviour (min/day)	numeric	None	Sedentary	Sedentary
hs_total_bread_Ter	hs_total_bread_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Food group: bread (hs_darkbread+hs_whbread)	factor	Tertiles	Bread	Bread
hs_total_cereal_Ter	hs_total_cereal_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Food group: cereal (hs_darkbread + hs_whbread + hs_rice_pasta + hs_sugarcer + hs_othcer + hs_rusks)	factor	Tertiles	Cereals	Cereals
hs_total_fish_Ter	hs_total_fish_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Food group: fish and seafood (hs_canfish+hs_oilyfish+hs_whfish+hs_seafood)	factor	Tertiles	Fish	Fish
hs_total_fruits_Ter	hs_total_fruits_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Food group: fruits (hs_canfruit+hs_dryfruit+hs_freshjuice+hs_fruits)	factor	Tertiles	Fruits	Fruits
hs_total_lipids_Ter	hs_total_lipids_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Food group: Added fat	factor	Tertiles	Diet fat	Diet fat
hs_total_meat_Ter	hs_total_meat_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Food group: meat (hs_coldmeat+hs_ham+hs_poultry+hs_redmeat)	factor	Tertiles	Meat	Meat
hs_total_potatoes_Ter	hs_total_potatoes_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Food group: potatoes (hs_frenchfries+hs_potatoes)	factor	Tertiles	Potatoes	Potatoes
hs_total_sweets_Ter	hs_total_sweets_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Food group: sweets (hs_choco + hs_sweets + hs_sugar)	factor	Tertiles	Sweets	Sweets
hs_total_veg_Ter	hs_total_veg_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Food group: vegetables (hs_cookveg+hs_rawveg)	factor	Tertiles	Vegetables	Vegetables
hs_total_yog_Ter	hs_total_yog_Ter	Lifestyles	Lifestyle	Diet	Postnatal	NA	NA	Food group: yogurt (hs_yogurt+hs_probiotic)	factor	Tertiles	Yogurt	Yogurt
hs_dif_hours_total_None	hs_dif_hours_total_None	Lifestyles	Lifestyle	Sleep	Postnatal	NA	NA	Total hours of sleep (mean weekdays and night)	numeric	None	Sleep	Sleep
hs_as_c_Log2	hs_as_c_Log2	Chemicals	Metals	As	Postnatal	NA	NA	Arsenic (As) in child	numeric	Logarithm base 2	As	As
hs_cd_c_Log2	hs_cd_c_Log2	Chemicals	Metals	Cd	Postnatal	NA	NA	Cadmium (Cd) in child	numeric	Logarithm base 2	Cd	Cd
hs_co_c_Log2	hs_co_c_Log2	Chemicals	Metals	Co	Postnatal	NA	NA	Cobalt (Co) in child	numeric	Logarithm base 2	Co	Co
hs_cs_c_Log2	hs_cs_c_Log2	Chemicals	Metals	Cs	Postnatal	NA	NA	Caesium (Cs) in child	numeric	Logarithm base 2	Cs	Cs
hs_cu_c_Log2	hs_cu_c_Log2	Chemicals	Metals	Cu	Postnatal	NA	NA	Copper (Cu) in child	numeric	Logarithm base 2	Cu	Cu
hs_hg_c_Log2	hs_hg_c_Log2	Chemicals	Metals	Hg	Postnatal	NA	NA	Mercury (Hg) in child	numeric	Logarithm base 2	Hg	Hg
hs_mn_c_Log2	hs_mn_c_Log2	Chemicals	Metals	Mn	Postnatal	NA	NA	Manganese (Mn) in child	numeric	Logarithm base 2	Mn	Mn
hs_mo_c_Log2	hs_mo_c_Log2	Chemicals	Metals	Mo	Postnatal	NA	NA	Molybdenum (Mo) in child	numeric	Logarithm base 2	Mo	Mo
hs_pb_c_Log2	hs_pb_c_Log2	Chemicals	Metals	Pb	Postnatal	NA	NA	Lead (Pb) in child	numeric	Logarithm base 2	Pb	Pb
hs_tl_cdich_None	hs_tl_cdich_None	Chemicals	Metals	Tl	Postnatal	NA	NA	Dichotomous variable of thallium (Tl) in child	factor	None	Tl	Tl
hs_dde_cadj_Log2	hs_dde_cadj_Log2	Chemicals	Organochlorines	DDE	Postnatal	NA	NA	Dichlorodiphenyldichloroethylene (DDE) in child adjusted for lipids	numeric	Logarithm base 2	DDE	DDE
hs_ddt_cadj_Log2	hs_ddt_cadj_Log2	Chemicals	Organochlorines	DDT	Postnatal	NA	NA	Dichlorodiphenyltrichloroethane (DDT) in child adjusted for lipids	numeric	Logarithm base 2	DDT	DDT
hs_hcb_cadj_Log2	hs_hcb_cadj_Log2	Chemicals	Organochlorines	HCB	Postnatal	NA	NA	Hexachlorobenzene (HCB) in child adjusted for lipids	numeric	Logarithm base 2	HCB	HCB
hs_pcb118_cadj_Log2	hs_pcb118_cadj_Log2	Chemicals	Organochlorines	PCBs	Postnatal	NA	NA	Polychlorinated biphenyl -118 (PCB-118) in child adjusted for lipids	numeric	Logarithm base 2	PCB 118	PCB118
hs_pcb138_cadj_Log2	hs_pcb138_cadj_Log2	Chemicals	Organochlorines	PCBs	Postnatal	NA	NA	Polychlorinated biphenyl-138 (PCB-138) in child adjusted for lipids	numeric	Logarithm base 2	PCB 138	PCB138
hs_pcb153_cadj_Log2	hs_pcb153_cadj_Log2	Chemicals	Organochlorines	PCBs	Postnatal	NA	NA	Polychlorinated biphenyl-153 (PCB-153) in child adjusted for lipids	numeric	Logarithm base 2	PCB 153	PCB153
hs_pcb170_cadj_Log2	hs_pcb170_cadj_Log2	Chemicals	Organochlorines	PCBs	Postnatal	NA	NA	Polychlorinated biphenyl-170 (PCB-170) in child adjusted for lipids	numeric	Logarithm base 2	PCB 170	PCB170
hs_pcb180_cadj_Log2	hs_pcb180_cadj_Log2	Chemicals	Organochlorines	PCBs	Postnatal	NA	NA	Polychlorinated biphenyl-180 (PCB-180) in child adjusted for lipids	numeric	Logarithm base 2	PCB 180	PCB180
hs_sumPCBs5_cadj_Log2	hs_sumPCBs5_cadj_Log2	Chemicals	Organochlorines	PCBs	Postnatal	NA	NA	Sum of PCBs in child adjusted for lipids (4 cohorts)	numeric	Logarithm base 2	PCBs	SumPCB
hs_dep_cadj_Log2	hs_dep_cadj_Log2	Chemicals	Organophosphate pesticides	DEP	Postnatal	NA	NA	Diethyl phosphate (DEP) in child adjusted for creatinine	numeric	Logarithm base 2	DEP	DEP
hs_detp_cadj_Log2	hs_detp_cadj_Log2	Chemicals	Organophosphate pesticides	DETP	Postnatal	NA	NA	Diethyl thiophosphate (DETP) in child adjusted for creatinine	numeric	Logarithm base 2	DETP	DETP
hs_dmdtp_cdich_None	hs_dmdtp_cdich_None	Chemicals	Organophosphate pesticides	DMDTP	Postnatal	NA	NA	Dichotomous variable of dimethyl dithiophosphate (DMDTP) in child	factor	None	DMDTP	DMDTP
hs_dmp_cadj_Log2	hs_dmp_cadj_Log2	Chemicals	Organophosphate pesticides	DMP	Postnatal	NA	NA	Dimethyl phosphate (DMP) in child adjusted for creatinine	numeric	Logarithm base 2	DMP	DMP
hs_dmtp_cadj_Log2	hs_dmtp_cadj_Log2	Chemicals	Organophosphate pesticides	DMTP	Postnatal	NA	NA	Dimethyl thiophosphate (DMTP) in child adjusted for creatinine	numeric	Logarithm base 2	DMDTP	DMTP
hs_pbde153_cadj_Log2	hs_pbde153_cadj_Log2	Chemicals	Polybrominated diphenyl ethers (PBDE)	PBDE153	Postnatal	NA	NA	Polybrominated diphenyl ether-153 (PBDE-153) in child adjusted for lipids	numeric	Logarithm base 2	PBDE 153	PBDE153
hs_pbde47_cadj_Log2	hs_pbde47_cadj_Log2	Chemicals	Polybrominated diphenyl ethers (PBDE)	PBDE47	Postnatal	NA	NA	Polybrominated diphenyl ether-47 (PBDE-47) in child adjusted for lipids	numeric	Logarithm base 2	PBDE 47	PBDE47
hs_pfhxs_c_Log2	hs_pfhxs_c_Log2	Chemicals	Per- and polyfluoroalkyl substances (PFAS)	PFHXS	Postnatal	NA	NA	Perfluorohexane sulfonate (PFHXS) in child	numeric	Logarithm base 2	PFHXS	PFHXS
hs_pfna_c_Log2	hs_pfna_c_Log2	Chemicals	Per- and polyfluoroalkyl substances (PFAS)	PFNA	Postnatal	NA	NA	Perfluorononanoate (PFNA) in child	numeric	Logarithm base 2	PFNA	PFNA
hs_pfoa_c_Log2	hs_pfoa_c_Log2	Chemicals	Per- and polyfluoroalkyl substances (PFAS)	PFOA	Postnatal	NA	NA	Perfluorooctanoate (PFOA) in child	numeric	Logarithm base 2	PFOA	PFOA
hs_pfos_c_Log2	hs_pfos_c_Log2	Chemicals	Per- and polyfluoroalkyl substances (PFAS)	PFOS	Postnatal	NA	NA	Perfluorooctane sulfonate (PFOS) in child	numeric	Logarithm base 2	PFOS	PFOS
hs_pfunda_c_Log2	hs_pfunda_c_Log2	Chemicals	Per- and polyfluoroalkyl substances (PFAS)	PFUNDA	Postnatal	NA	NA	Perfluoroundecanoate (PFUNDA) in child	numeric	Logarithm base 2	PFUNDA	PFUNDA
hs_bpa_cadj_Log2	hs_bpa_cadj_Log2	Chemicals	Phenols	BPA	Postnatal	NA	NA	Bisphenol A (BPA) in child adjusted for creatinine	numeric	Logarithm base 2	BPA	BPA
hs_bupa_cadj_Log2	hs_bupa_cadj_Log2	Chemicals	Phenols	BUPA	Postnatal	NA	NA	N-Butyl paraben (BUPA) in child adjusted for creatinine	numeric	Logarithm base 2	BUPA	BUPA
hs_etpa_cadj_Log2	hs_etpa_cadj_Log2	Chemicals	Phenols	ETPA	Postnatal	NA	NA	Ethyl paraben (ETPA) in child adjusted for creatinine	numeric	Logarithm base 2	ETPA	ETPA
hs_mepa_cadj_Log2	hs_mepa_cadj_Log2	Chemicals	Phenols	MEPA	Postnatal	NA	NA	Methyl paraben (MEPA) in child adjusted for creatinine	numeric	Logarithm base 2	MEPA	MEPA
hs_oxbe_cadj_Log2	hs_oxbe_cadj_Log2	Chemicals	Phenols	OXBE	Postnatal	NA	NA	Oxybenzone (OXBE) in child adjusted for creatinine	numeric	Logarithm base 2	OXBE	OXBE
hs_prpa_cadj_Log2	hs_prpa_cadj_Log2	Chemicals	Phenols	PRPA	Postnatal	NA	NA	Propyl paraben (PRPA) in child adjusted for creatinine	numeric	Logarithm base 2	PRPA	PRPA
hs_trcs_cadj_Log2	hs_trcs_cadj_Log2	Chemicals	Phenols	TRCS	Postnatal	NA	NA	Triclosan (TRCS) in child adjusted for creatinine	numeric	Logarithm base 2	TRCS	TRCS
hs_mbzp_cadj_Log2	hs_mbzp_cadj_Log2	Chemicals	Phthalates	MBZP	Postnatal	NA	NA	Mono benzyl phthalate (MBzP) in child adjusted for creatinine	numeric	Logarithm base 2	MBZP	MBZP
hs_mecpp_cadj_Log2	hs_mecpp_cadj_Log2	Chemicals	Phthalates	MECPP	Postnatal	NA	NA	Mono-2-ethyl 5-carboxypentyl phthalate (MECPP) in child adjusted for creatinine	numeric	Logarithm base 2	MECPP	MECPP
hs_mehhp_cadj_Log2	hs_mehhp_cadj_Log2	Chemicals	Phthalates	MEHHP	Postnatal	NA	NA	Mono-2-ethyl-5-hydroxyhexyl phthalate (MEHHP) in child adjusted for creatinine	numeric	Logarithm base 2	MEHHP	MEHHP
hs_mehp_cadj_Log2	hs_mehp_cadj_Log2	Chemicals	Phthalates	MEHP	Postnatal	NA	NA	Mono-2-ethylhexyl phthalate (MEHP) in child adjusted for creatinine	numeric	Logarithm base 2	MEHP	MEHP
hs_meohp_cadj_Log2	hs_meohp_cadj_Log2	Chemicals	Phthalates	MEOHP	Postnatal	NA	NA	Mono-2-ethyl-5-oxohexyl phthalate (MEOHP) in child adjusted for creatinine	numeric	Logarithm base 2	MEOHP	MEOHP
hs_mep_cadj_Log2	hs_mep_cadj_Log2	Chemicals	Phthalates	MEP	Postnatal	NA	NA	Monoethyl phthalate (MEP) in child adjusted for creatinine	numeric	Logarithm base 2	MEP	MEP
hs_mibp_cadj_Log2	hs_mibp_cadj_Log2	Chemicals	Phthalates	MIBP	Postnatal	NA	NA	Mono-iso-butyl phthalate (MiBP) in child adjusted for creatinine	numeric	Logarithm base 2	MIBP	MIBP
hs_mnbp_cadj_Log2	hs_mnbp_cadj_Log2	Chemicals	Phthalates	MNBP	Postnatal	NA	NA	Mono-n-butyl phthalate (MnBP) in child adjusted for creatinine	numeric	Logarithm base 2	MNBP	MNBP
hs_ohminp_cadj_Log2	hs_ohminp_cadj_Log2	Chemicals	Phthalates	OHMiNP	Postnatal	NA	NA	Mono-4-methyl-7-hydroxyoctyl phthalate (OHMiNP) in child adjusted for creatinine	numeric	Logarithm base 2	OHMiNP	OHMiNP
hs_oxominp_cadj_Log2	hs_oxominp_cadj_Log2	Chemicals	Phthalates	OXOMINP	Postnatal	NA	NA	Mono-4-methyl-7-oxooctyl phthalate (OXOMiNP) in child adjusted for creatinine	numeric	Logarithm base 2	OXOMINP	OXOMINP
hs_sumDEHP_cadj_Log2	hs_sumDEHP_cadj_Log2	Chemicals	Phthalates	DEHP	Postnatal	NA	NA	Sum of DEHP metabolites (µg/g) in child adjusted for creatinine	numeric	Logarithm base 2	DEHP	SumDEHP
FAS_cat_None	FAS_cat_None	Chemicals	Social and economic capital	Economic capital	Postnatal	NA	NA	Family affluence score	factor	None	Family affluence	FamAfl
hs_contactfam_3cat_num_None	hs_contactfam_3cat_num_None	Chemicals	Social and economic capital	Social capital	Postnatal	NA	NA	scoial capital: family friends	factor	None	Social contact	SocCont
hs_hm_pers_None	hs_hm_pers_None	Chemicals	Social and economic capital	Social capital	Postnatal	NA	NA	How many people live in your home?	numeric	None	House crowding	HouseCrow
hs_participation_3cat_None	hs_participation_3cat_None	Chemicals	Social and economic capital	Social capital	Postnatal	NA	NA	social capital: structural	factor	None	Social participation	SocPartic
hs_cotinine_cdich_None	hs_cotinine_cdich_None	Chemicals	Tobacco Smoke	Cotinine	Postnatal	NA	NA	Dichotomous variable of cotinine in child	factor	None	Cotinine	Cotinine
hs_globalexp2_None	hs_globalexp2_None	Chemicals	Tobacco Smoke	Tobacco Smoke	Postnatal	NA	NA	Global exposure of the child to ETS (2 categories)	factor	None	ETS	ETS
hs_smk_parents_None	hs_smk_parents_None	Chemicals	Tobacco Smoke	Tobacco Smoke	Postnatal	NA	NA	Tobacco Smoke status of parents (both)	factor	None	Smoking_parents	SmokPar

Data Summary for Exposures, Covariates, and Outcome

Data Summary Exposures: Lifestyles

Lifestyle_Exposures <- filtered_codebook$variable_name[filtered_codebook$domain=="Lifestyles"]
lifestyle_exposome <- exposome %>%
  select(all_of(Lifestyle_Exposures))
summarytools::view(dfSummary(lifestyle_exposome, style = 'grid', plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")

No	Variable	Stats / Values	Freqs (% of Valid)	Graph	Missing
1	h_bfdur_Ter [factor]	1. (0,10.8] 2. (10.8,34.9] 3. (34.9,Inf]	506 ( 38.9% ) 270 ( 20.8% ) 525 ( 40.4% )		0 (0.0%)
2	hs_bakery_prod_Ter [factor]	1. (0,2] 2. (2,6] 3. (6,Inf]	345 ( 26.5% ) 423 ( 32.5% ) 533 ( 41.0% )		0 (0.0%)
3	hs_beverages_Ter [factor]	1. (0,0.132] 2. (0.132,1] 3. (1,Inf]	331 ( 25.4% ) 454 ( 34.9% ) 516 ( 39.7% )		0 (0.0%)
4	hs_break_cer_Ter [factor]	1. (0,1.1] 2. (1.1,5.5] 3. (5.5,Inf]	291 ( 22.4% ) 521 ( 40.0% ) 489 ( 37.6% )		0 (0.0%)
5	hs_caff_drink_Ter [factor]	1. (0,0.132] 2. (0.132,Inf]	808 ( 62.1% ) 493 ( 37.9% )		0 (0.0%)
6	hs_dairy_Ter [factor]	1. (0,14.6] 2. (14.6,25.6] 3. (25.6,Inf]	359 ( 27.6% ) 465 ( 35.7% ) 477 ( 36.7% )		0 (0.0%)
7	hs_fastfood_Ter [factor]	1. (0,0.132] 2. (0.132,0.5] 3. (0.5,Inf]	143 ( 11.0% ) 603 ( 46.3% ) 555 ( 42.7% )		0 (0.0%)
8	hs_KIDMED_None [numeric]	Mean (sd) : 2.9 (1.8) min ≤ med ≤ max: -3 ≤ 3 ≤ 9 IQR (CV) : 2 (0.6)	13 distinct values		0 (0.0%)
9	hs_mvpa_prd_alt_None [numeric]	Mean (sd) : 37.9 (23.1) min ≤ med ≤ max: -27.8 ≤ 34.7 ≤ 146.8 IQR (CV) : 24.5 (0.6)	847 distinct values		0 (0.0%)
10	hs_org_food_Ter [factor]	1. (0,0.132] 2. (0.132,1] 3. (1,Inf]	429 ( 33.0% ) 396 ( 30.4% ) 476 ( 36.6% )		0 (0.0%)
11	hs_proc_meat_Ter [factor]	1. (0,1.5] 2. (1.5,4] 3. (4,Inf]	366 ( 28.1% ) 471 ( 36.2% ) 464 ( 35.7% )		0 (0.0%)
12	hs_readymade_Ter [factor]	1. (0,0.132] 2. (0.132,0.5] 3. (0.5,Inf]	327 ( 25.1% ) 296 ( 22.8% ) 678 ( 52.1% )		0 (0.0%)
13	hs_sd_wk_None [numeric]	Mean (sd) : 235.8 (126.7) min ≤ med ≤ max: 3.1 ≤ 210 ≤ 994.3 IQR (CV) : 127.1 (0.5)	368 distinct values		0 (0.0%)
14	hs_total_bread_Ter [factor]	1. (0,7] 2. (7,17.5] 3. (17.5,Inf]	431 ( 33.1% ) 381 ( 29.3% ) 489 ( 37.6% )		0 (0.0%)
15	hs_total_cereal_Ter [factor]	1. (0,14.1] 2. (14.1,23.6] 3. (23.6,Inf]	418 ( 32.1% ) 442 ( 34.0% ) 441 ( 33.9% )		0 (0.0%)
16	hs_total_fish_Ter [factor]	1. (0,1.5] 2. (1.5,3] 3. (3,Inf]	389 ( 29.9% ) 454 ( 34.9% ) 458 ( 35.2% )		0 (0.0%)
17	hs_total_fruits_Ter [factor]	1. (0,7] 2. (7,14.1] 3. (14.1,Inf]	413 ( 31.7% ) 407 ( 31.3% ) 481 ( 37.0% )		0 (0.0%)
18	hs_total_lipids_Ter [factor]	1. (0,3] 2. (3,7] 3. (7,Inf]	397 ( 30.5% ) 403 ( 31.0% ) 501 ( 38.5% )		0 (0.0%)
19	hs_total_meat_Ter [factor]	1. (0,6] 2. (6,9] 3. (9,Inf]	425 ( 32.7% ) 411 ( 31.6% ) 465 ( 35.7% )		0 (0.0%)
20	hs_total_potatoes_Ter [factor]	1. (0,3] 2. (3,4] 3. (4,Inf]	417 ( 32.1% ) 405 ( 31.1% ) 479 ( 36.8% )		0 (0.0%)
21	hs_total_sweets_Ter [factor]	1. (0,4.1] 2. (4.1,8.5] 3. (8.5,Inf]	344 ( 26.4% ) 516 ( 39.7% ) 441 ( 33.9% )		0 (0.0%)
22	hs_total_veg_Ter [factor]	1. (0,6] 2. (6,8.5] 3. (8.5,Inf]	404 ( 31.1% ) 314 ( 24.1% ) 583 ( 44.8% )		0 (0.0%)
23	hs_total_yog_Ter [factor]	1. (0,6] 2. (6,8.5] 3. (8.5,Inf]	779 ( 59.9% ) 308 ( 23.7% ) 214 ( 16.4% )		0 (0.0%)
24	hs_dif_hours_total_None [numeric]	Mean (sd) : 10.3 (0.7) min ≤ med ≤ max: 7.9 ≤ 10.3 ≤ 12.9 IQR (CV) : 0.9 (0.1)	437 distinct values		0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.4.0)
2024-06-10

#separate numeric and categorical data
numeric_lifestyle <- lifestyle_exposome %>% 
  select(where(is.numeric))

numeric_lifestyle_long <- pivot_longer(
  numeric_lifestyle,
  cols = everything(),
  names_to = "variable",
  values_to = "value"
)

unique_numerical_vars <- unique(numeric_lifestyle_long$variable)

num_plots <- lapply(unique_numerical_vars, function(var) {
  data <- filter(numeric_lifestyle_long, variable == var)
  p <- ggplot(data, aes(x = value)) +
    geom_histogram(bins = 30, fill = "blue") +
    labs(title = paste("Histogram of", var), x = "Value", y = "Count")
  print(p)
  return(p)
})

categorical_lifestyle <- lifestyle_exposome %>% 
  select(where(is.factor))

categorical_lifestyle_long <- pivot_longer(
  categorical_lifestyle,
  cols = everything(),
  names_to = "variable",
  values_to = "value"
)

unique_categorical_vars <- unique(categorical_lifestyle_long$variable)
categorical_plots <- lapply(unique_categorical_vars, function(var) {
  data <- filter(categorical_lifestyle_long, variable == var)
  
  p <- ggplot(data, aes(x = value, fill = value)) +
    geom_bar(stat = "count") +
    labs(title = paste("Distribution of", var), x = var, y = "Count")
  
  print(p)
  return(p)
})

numeric_lifestyle <- select_if(lifestyle_exposome, is.numeric)
cor_matrix <- cor(numeric_lifestyle, method = "pearson")
cor_matrix <- cor(numeric_lifestyle, method = "spearman")
corrplot(cor_matrix, method = "circle")

Data Summary Exposures: Chemicals

Chemical_Exposures <- filtered_codebook$variable_name[filtered_codebook$domain=="Chemicals"]
chemical_exposome <- exposome %>%
  select(all_of(Chemical_Exposures))
summarytools::view(dfSummary(chemical_exposome, style = 'grid', plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")

No	Variable	Stats / Values	Freqs (% of Valid)	Graph	Missing
1	hs_as_c_Log2 [numeric]	Mean (sd) : -1 (3.3) min ≤ med ≤ max: -15 ≤ 0.5 ≤ 4.8 IQR (CV) : 5.3 (-3.3)	692 distinct values		0 (0.0%)
2	hs_cd_c_Log2 [numeric]	Mean (sd) : -4 (1) min ≤ med ≤ max: -10.4 ≤ -3.8 ≤ 0.8 IQR (CV) : 1 (-0.3)	695 distinct values		0 (0.0%)
3	hs_co_c_Log2 [numeric]	Mean (sd) : -2.3 (0.6) min ≤ med ≤ max: -5.5 ≤ -2.4 ≤ 1.4 IQR (CV) : 0.7 (-0.3)	317 distinct values		0 (0.0%)
4	hs_cs_c_Log2 [numeric]	Mean (sd) : 0.4 (0.6) min ≤ med ≤ max: -1.5 ≤ 0.5 ≤ 3.1 IQR (CV) : 0.8 (1.3)	369 distinct values		0 (0.0%)
5	hs_cu_c_Log2 [numeric]	Mean (sd) : 9.8 (0.2) min ≤ med ≤ max: 9.1 ≤ 9.8 ≤ 12.1 IQR (CV) : 0.3 (0)	345 distinct values		0 (0.0%)
6	hs_hg_c_Log2 [numeric]	Mean (sd) : -0.3 (1.7) min ≤ med ≤ max: -10.9 ≤ -0.2 ≤ 3.7 IQR (CV) : 2.1 (-5.6)	698 distinct values		0 (0.0%)
7	hs_mn_c_Log2 [numeric]	Mean (sd) : 3.1 (0.4) min ≤ med ≤ max: 1.7 ≤ 3.1 ≤ 4.8 IQR (CV) : 0.6 (0.1)	457 distinct values		0 (0.0%)
8	hs_mo_c_Log2 [numeric]	Mean (sd) : -0.3 (0.9) min ≤ med ≤ max: -9.2 ≤ -0.4 ≤ 5.1 IQR (CV) : 0.8 (-2.9)	593 distinct values		0 (0.0%)
9	hs_pb_c_Log2 [numeric]	Mean (sd) : 3.1 (0.6) min ≤ med ≤ max: 1.1 ≤ 3.1 ≤ 7.7 IQR (CV) : 0.8 (0.2)	529 distinct values		0 (0.0%)
10	hs_tl_cdich_None [factor]	1. Detected 2. Undetected	102 ( 7.8% ) 1199 ( 92.2% )		0 (0.0%)
11	hs_dde_cadj_Log2 [numeric]	Mean (sd) : 4.7 (1.5) min ≤ med ≤ max: 1.2 ≤ 4.5 ≤ 11.1 IQR (CV) : 1.9 (0.3)	1050 distinct values		0 (0.0%)
12	hs_ddt_cadj_Log2 [numeric]	Mean (sd) : -1.6 (3.7) min ≤ med ≤ max: -15.4 ≤ -0.5 ≤ 7.6 IQR (CV) : 2.5 (-2.3)	1039 distinct values		0 (0.0%)
13	hs_hcb_cadj_Log2 [numeric]	Mean (sd) : 3.2 (0.9) min ≤ med ≤ max: -13.1 ≤ 3.1 ≤ 6.5 IQR (CV) : 0.9 (0.3)	1036 distinct values		0 (0.0%)
14	hs_pcb118_cadj_Log2 [numeric]	Mean (sd) : 1.1 (0.8) min ≤ med ≤ max: -7 ≤ 1 ≤ 4.8 IQR (CV) : 1 (0.7)	1048 distinct values		0 (0.0%)
15	hs_pcb138_cadj_Log2 [numeric]	Mean (sd) : 2.4 (1.1) min ≤ med ≤ max: -9.4 ≤ 2.4 ≤ 7.7 IQR (CV) : 1.4 (0.5)	1031 distinct values		0 (0.0%)
16	hs_pcb153_cadj_Log2 [numeric]	Mean (sd) : 3.6 (0.9) min ≤ med ≤ max: 1.2 ≤ 3.5 ≤ 7.8 IQR (CV) : 1.4 (0.3)	1047 distinct values		0 (0.0%)
17	hs_pcb170_cadj_Log2 [numeric]	Mean (sd) : -0.3 (3) min ≤ med ≤ max: -16.8 ≤ 0.3 ≤ 4.8 IQR (CV) : 2.2 (-9.8)	1039 distinct values		0 (0.0%)
18	hs_pcb180_cadj_Log2 [numeric]	Mean (sd) : 1.7 (1.9) min ≤ med ≤ max: -11.7 ≤ 1.8 ≤ 5.9 IQR (CV) : 2.3 (1.1)	1055 distinct values		0 (0.0%)
19	hs_sumPCBs5_cadj_Log2 [numeric]	Mean (sd) : 4.6 (1) min ≤ med ≤ max: 2.2 ≤ 4.6 ≤ 9.3 IQR (CV) : 1.5 (0.2)	1052 distinct values		0 (0.0%)
20	hs_dep_cadj_Log2 [numeric]	Mean (sd) : 0.2 (3.2) min ≤ med ≤ max: -12.6 ≤ 0.9 ≤ 9.4 IQR (CV) : 3.3 (20)	1045 distinct values		0 (0.0%)
21	hs_detp_cadj_Log2 [numeric]	Mean (sd) : -2.4 (3.6) min ≤ med ≤ max: -15.4 ≤ -3.3 ≤ 6.3 IQR (CV) : 6 (-1.5)	1036 distinct values		0 (0.0%)
22	hs_dmdtp_cdich_None [factor]	1. Detected 2. Undetected	227 ( 17.4% ) 1074 ( 82.6% )		0 (0.0%)
23	hs_dmp_cadj_Log2 [numeric]	Mean (sd) : -1.4 (4) min ≤ med ≤ max: -16.6 ≤ -0.3 ≤ 6.4 IQR (CV) : 7 (-2.9)	1053 distinct values		0 (0.0%)
24	hs_dmtp_cadj_Log2 [numeric]	Mean (sd) : 1.1 (2.6) min ≤ med ≤ max: -10.6 ≤ 1.6 ≤ 8.7 IQR (CV) : 2.4 (2.3)	1057 distinct values		0 (0.0%)
25	hs_pbde153_cadj_Log2 [numeric]	Mean (sd) : -4.5 (3.8) min ≤ med ≤ max: -17.6 ≤ -2.6 ≤ 4 IQR (CV) : 6.7 (-0.8)	1036 distinct values		0 (0.0%)
26	hs_pbde47_cadj_Log2 [numeric]	Mean (sd) : -2.6 (2.5) min ≤ med ≤ max: -15.4 ≤ -2.1 ≤ 5.4 IQR (CV) : 1.2 (-1)	1010 distinct values		0 (0.0%)
27	hs_pfhxs_c_Log2 [numeric]	Mean (sd) : -1.6 (1.3) min ≤ med ≤ max: -8.9 ≤ -1.4 ≤ 4.8 IQR (CV) : 1.7 (-0.8)	1061 distinct values		0 (0.0%)
28	hs_pfna_c_Log2 [numeric]	Mean (sd) : -1.1 (1.1) min ≤ med ≤ max: -8.1 ≤ -1.1 ≤ 2.7 IQR (CV) : 1.3 (-1)	1031 distinct values		0 (0.0%)
29	hs_pfoa_c_Log2 [numeric]	Mean (sd) : 0.6 (0.6) min ≤ med ≤ max: -2.2 ≤ 0.6 ≤ 2.7 IQR (CV) : 0.7 (0.9)	1061 distinct values		0 (0.0%)
30	hs_pfos_c_Log2 [numeric]	Mean (sd) : 1 (1.1) min ≤ med ≤ max: -10.4 ≤ 1 ≤ 5.1 IQR (CV) : 1.3 (1.1)	1050 distinct values		0 (0.0%)
31	hs_pfunda_c_Log2 [numeric]	Mean (sd) : -4.2 (1.6) min ≤ med ≤ max: -11.8 ≤ -4.1 ≤ 0.6 IQR (CV) : 1.7 (-0.4)	1044 distinct values		0 (0.0%)
32	hs_bpa_cadj_Log2 [numeric]	Mean (sd) : 2.1 (1.5) min ≤ med ≤ max: -7.2 ≤ 2 ≤ 7.8 IQR (CV) : 1.6 (0.7)	1056 distinct values		0 (0.0%)
33	hs_bupa_cadj_Log2 [numeric]	Mean (sd) : -3.5 (2) min ≤ med ≤ max: -13.9 ≤ -3.5 ≤ 6.6 IQR (CV) : 1.8 (-0.6)	1034 distinct values		0 (0.0%)
34	hs_etpa_cadj_Log2 [numeric]	Mean (sd) : -0.1 (1.9) min ≤ med ≤ max: -6.1 ≤ -0.6 ≤ 11 IQR (CV) : 1.6 (-14.3)	1066 distinct values		0 (0.0%)
35	hs_mepa_cadj_Log2 [numeric]	Mean (sd) : 3.4 (2.5) min ≤ med ≤ max: -6.9 ≤ 2.7 ≤ 14.5 IQR (CV) : 3 (0.7)	1052 distinct values		0 (0.0%)
36	hs_oxbe_cadj_Log2 [numeric]	Mean (sd) : 1.5 (2.4) min ≤ med ≤ max: -4.1 ≤ 1.1 ≤ 13 IQR (CV) : 3 (1.6)	1069 distinct values		0 (0.0%)
37	hs_prpa_cadj_Log2 [numeric]	Mean (sd) : -1.6 (3.8) min ≤ med ≤ max: -12 ≤ -2.3 ≤ 10.8 IQR (CV) : 5.2 (-2.4)	1031 distinct values		0 (0.0%)
38	hs_trcs_cadj_Log2 [numeric]	Mean (sd) : -0.4 (2) min ≤ med ≤ max: -4.4 ≤ -0.7 ≤ 9.3 IQR (CV) : 2.2 (-5.6)	1053 distinct values		0 (0.0%)
39	hs_mbzp_cadj_Log2 [numeric]	Mean (sd) : 2.4 (1.2) min ≤ med ≤ max: -0.6 ≤ 2.3 ≤ 7.2 IQR (CV) : 1.5 (0.5)	1046 distinct values		0 (0.0%)
40	hs_mecpp_cadj_Log2 [numeric]	Mean (sd) : 5.2 (1.1) min ≤ med ≤ max: 2.6 ≤ 5.1 ≤ 10.6 IQR (CV) : 1.5 (0.2)	1037 distinct values		0 (0.0%)
41	hs_mehhp_cadj_Log2 [numeric]	Mean (sd) : 4.4 (1.1) min ≤ med ≤ max: 1.8 ≤ 4.4 ≤ 11.1 IQR (CV) : 1.4 (0.2)	1050 distinct values		0 (0.0%)
42	hs_mehp_cadj_Log2 [numeric]	Mean (sd) : 1.6 (1.2) min ≤ med ≤ max: -1.6 ≤ 1.6 ≤ 8.1 IQR (CV) : 1.5 (0.7)	1035 distinct values		0 (0.0%)
43	hs_meohp_cadj_Log2 [numeric]	Mean (sd) : 3.7 (1.1) min ≤ med ≤ max: 1.1 ≤ 3.6 ≤ 10.3 IQR (CV) : 1.5 (0.3)	1057 distinct values		0 (0.0%)
44	hs_mep_cadj_Log2 [numeric]	Mean (sd) : 5.3 (1.6) min ≤ med ≤ max: 1.7 ≤ 5.1 ≤ 11.6 IQR (CV) : 2.2 (0.3)	1075 distinct values		0 (0.0%)
45	hs_mibp_cadj_Log2 [numeric]	Mean (sd) : 5.5 (1.1) min ≤ med ≤ max: 2.3 ≤ 5.4 ≤ 9.8 IQR (CV) : 1.5 (0.2)	1057 distinct values		0 (0.0%)
46	hs_mnbp_cadj_Log2 [numeric]	Mean (sd) : 4.7 (1) min ≤ med ≤ max: 1.9 ≤ 4.6 ≤ 8.9 IQR (CV) : 1.3 (0.2)	1048 distinct values		0 (0.0%)
47	hs_ohminp_cadj_Log2 [numeric]	Mean (sd) : 2.6 (1.2) min ≤ med ≤ max: -0.3 ≤ 2.4 ≤ 9.1 IQR (CV) : 1.5 (0.5)	1085 distinct values		0 (0.0%)
48	hs_oxominp_cadj_Log2 [numeric]	Mean (sd) : 1.7 (1.2) min ≤ med ≤ max: -0.9 ≤ 1.5 ≤ 9.4 IQR (CV) : 1.4 (0.7)	1059 distinct values		0 (0.0%)
49	hs_sumDEHP_cadj_Log2 [numeric]	Mean (sd) : 6 (1.2) min ≤ med ≤ max: 2.6 ≤ 6 ≤ 10.1 IQR (CV) : 1.6 (0.2)	1028 distinct values		0 (0.0%)
50	FAS_cat_None [factor]	1. Low 2. Middle 3. High	146 ( 11.2% ) 486 ( 37.4% ) 669 ( 51.4% )		0 (0.0%)
51	hs_contactfam_3cat_num_None [factor]	1. (almost) Daily 2. Once a week 3. Less than once a week	863 ( 66.3% ) 382 ( 29.4% ) 56 ( 4.3% )		0 (0.0%)
52	hs_hm_pers_None [numeric]	Mean (sd) : 4.2 (1) min ≤ med ≤ max: 1 ≤ 4 ≤ 10 IQR (CV) : 1 (0.2)	1 : 2 ( 0.2% ) 2 : 36 ( 2.8% ) 3 : 180 ( 13.8% ) 4 : 670 ( 51.5% ) 5 : 297 ( 22.8% ) 6 : 85 ( 6.5% ) 7 : 17 ( 1.3% ) 8 : 8 ( 0.6% ) 9 : 5 ( 0.4% ) 10 : 1 ( 0.1% )		0 (0.0%)
53	hs_participation_3cat_None [factor]	1. None 2. 1 organisation 3. 2 or more organisations	748 ( 57.5% ) 355 ( 27.3% ) 198 ( 15.2% )		0 (0.0%)
54	hs_cotinine_cdich_None [factor]	1. Detected 2. Undetected	223 ( 17.1% ) 1078 ( 82.9% )		0 (0.0%)
55	hs_globalexp2_None [factor]	1. exposure 2. no exposure	463 ( 35.6% ) 838 ( 64.4% )		0 (0.0%)
56	hs_smk_parents_None [factor]	1. both 2. neither 3. one	142 ( 10.9% ) 814 ( 62.6% ) 345 ( 26.5% )		0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.4.0)
2024-06-10

#separate numeric and categorical data
numeric_chemical <- chemical_exposome %>% 
  select(where(is.numeric))

numeric_chemical_long <- pivot_longer(
  numeric_chemical,
  cols = everything(),
  names_to = "variable",
  values_to = "value"
)

unique_numerical_vars <- unique(numeric_chemical_long$variable)

num_plots <- lapply(unique_numerical_vars, function(var) {
  data <- filter(numeric_chemical_long, variable == var)
  p <- ggplot(data, aes(x = value)) +
    geom_histogram(bins = 30, fill = "blue") +
    labs(title = paste("Histogram of", var), x = "Value", y = "Count")
  print(p)
  return(p)
})

categorical_chemical <- chemical_exposome %>% 
  select(where(is.factor))

categorical_chemical_long <- pivot_longer(
  categorical_lifestyle,
  cols = everything(),
  names_to = "variable",
  values_to = "value"
)

unique_categorical_vars <- unique(categorical_chemical_long$variable)
categorical_plots <- lapply(unique_categorical_vars, function(var) {
  data <- filter(categorical_chemical_long, variable == var)
  
  p <- ggplot(data, aes(x = value, fill = value)) +
    geom_bar(stat = "count") +
    labs(title = paste("Distribution of", var), x = var, y = "Count")
  
  print(p)
  return(p)
})

numeric_chemical <- select_if(chemical_exposome, is.numeric)
cor_matrix <- cor(numeric_chemical, method = "pearson")
cor_matrix <- cor(numeric_chemical, method = "spearman")
custom_color_scale <- list(
  c(0, "darkred"),    
  c(0.5, "white"), 
  c(1, "darkblue")
)

plot_ly(
  z = cor_matrix, 
  x = colnames(cor_matrix), 
  y = colnames(cor_matrix), 
  type = "heatmap",
  colorscale = custom_color_scale
) %>%
layout(
  title = "Correlation Matrix",
  xaxis = list(tickangle = -90),
  yaxis = list(side = "left")
)

Data Summary Covariates

summarytools::view(dfSummary(covariates, style = 'grid', plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")

No	Variable	Stats / Values	Freqs (% of Valid)	Graph	Missing
1	ID [integer]	Mean (sd) : 651 (375.7) min ≤ med ≤ max: 1 ≤ 651 ≤ 1301 IQR (CV) : 650 (0.6)	1301 distinct values (Integer sequence)		0 (0.0%)
2	h_cohort [factor]	1. 1 2. 2 3. 3 4. 4 5. 5 6. 6	202 ( 15.5% ) 198 ( 15.2% ) 224 ( 17.2% ) 207 ( 15.9% ) 272 ( 20.9% ) 198 ( 15.2% )		0 (0.0%)
3	e3_sex_None [factor]	1. female 2. male	608 ( 46.7% ) 693 ( 53.3% )		0 (0.0%)
4	e3_yearbir_None [factor]	1. 2003 2. 2004 3. 2005 4. 2006 5. 2007 6. 2008 7. 2009	55 ( 4.2% ) 107 ( 8.2% ) 241 ( 18.5% ) 256 ( 19.7% ) 250 ( 19.2% ) 379 ( 29.1% ) 13 ( 1.0% )		0 (0.0%)
5	h_mbmi_None [numeric]	Mean (sd) : 25 (5.2) min ≤ med ≤ max: 15.9 ≤ 24 ≤ 51.4 IQR (CV) : 6.1 (0.2)	853 distinct values		0 (0.0%)
6	hs_wgtgain_None [numeric]	Mean (sd) : 13.5 (6.2) min ≤ med ≤ max: 0 ≤ 12 ≤ 55 IQR (CV) : 9 (0.5)	49 distinct values		0 (0.0%)
7	e3_gac_None [numeric]	Mean (sd) : 39.6 (1.7) min ≤ med ≤ max: 28 ≤ 40 ≤ 44.1 IQR (CV) : 2 (0)	72 distinct values		0 (0.0%)
8	h_age_None [numeric]	Mean (sd) : 30.8 (4.9) min ≤ med ≤ max: 16 ≤ 31 ≤ 43.5 IQR (CV) : 6.4 (0.2)	665 distinct values		0 (0.0%)
9	h_edumc_None [factor]	1. 1 2. 2 3. 3	178 ( 13.7% ) 449 ( 34.5% ) 674 ( 51.8% )		0 (0.0%)
10	h_native_None [factor]	1. 0 2. 1 3. 2	146 ( 11.2% ) 67 ( 5.1% ) 1088 ( 83.6% )		0 (0.0%)
11	h_parity_None [factor]	1. 0 2. 1 3. 2	601 ( 46.2% ) 464 ( 35.7% ) 236 ( 18.1% )		0 (0.0%)
12	hs_child_age_None [numeric]	Mean (sd) : 8 (1.6) min ≤ med ≤ max: 5.4 ≤ 8 ≤ 12.1 IQR (CV) : 2.4 (0.2)	879 distinct values		0 (0.0%)
13	hs_c_height_None [numeric]	Mean (sd) : 1.3 (0.1) min ≤ med ≤ max: 1.1 ≤ 1.3 ≤ 1.7 IQR (CV) : 0.2 (0.1)	311 distinct values		0 (0.0%)
14	hs_c_weight_None [numeric]	Mean (sd) : 28.5 (7.7) min ≤ med ≤ max: 16 ≤ 26.9 ≤ 71.1 IQR (CV) : 9.8 (0.3)	311 distinct values		0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.4.0)
2024-06-10

#separate numeric and categorical data
numeric_covariates <- covariates %>% 
  select(where(is.numeric))

numeric_covariates_long <- pivot_longer(
  numeric_covariates,
  cols = everything(),
  names_to = "variable",
  values_to = "value"
)

unique_numerical_vars <- unique(numeric_covariates_long$variable)

num_plots <- lapply(unique_numerical_vars, function(var) {
  data <- filter(numeric_covariates_long, variable == var)
  p <- ggplot(data, aes(x = value)) +
    geom_histogram(bins = 30, fill = "blue") +
    labs(title = paste("Histogram of", var), x = "Value", y = "Count")
  print(p)
  return(p)
})

categorical_covariates <- covariates %>% 
  select(where(is.factor))

categorical_covariates_long <- pivot_longer(
  categorical_covariates,
  cols = everything(),
  names_to = "variable",
  values_to = "value"
)

unique_categorical_vars <- unique(categorical_covariates_long$variable)
categorical_plots <- lapply(unique_categorical_vars, function(var) {
  data <- filter(categorical_covariates_long, variable == var)
  
  p <- ggplot(data, aes(x = value, fill = value)) +
    geom_bar(stat = "count") +
    labs(title = paste("Distribution of", var), x = var, y = "Count")
  
  print(p)
  return(p)
})

numeric_covariate <- select_if(covariates, is.numeric)
cor_matrix <- cor(numeric_covariate, method = "pearson")
cor_matrix <- cor(numeric_covariate, method = "spearman")
corrplot(cor_matrix, method = "circle")

Data Summary Outcome: Phenotype

outcome_BMI <- phenotype %>% 
  select(hs_zbmi_who)
summarytools::view(dfSummary(outcome_BMI, style = 'grid', plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")

No	Variable	Stats / Values	Freqs (% of Valid)	Graph	Missing
1	hs_zbmi_who [numeric]	Mean (sd) : 0.4 (1.2) min ≤ med ≤ max: -3.6 ≤ 0.3 ≤ 4.7 IQR (CV) : 1.5 (3)	421 distinct values		0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.4.0)
2024-06-10

Metabolomic Serum Data

First 10 rows and columns of the metabolomic serum data

load(paste0(work.dir, "/metabol_serum.RData"))
kable(metabol_serum.d[1:10,1:10], align="c", digits=2, format="pipe")

	430	1187	940	936	788	698	380	196	114	885
metab_1	-2.15	-0.69	-0.69	-0.19	-1.96	-1.90	-0.22	-1.38	-0.54	-1.25
metab_2	-0.71	-0.37	-0.36	-0.34	-0.35	-0.63	-0.26	-0.46	-0.44	-0.48
metab_3	8.60	9.15	8.95	8.54	8.73	8.24	9.03	8.29	8.37	8.18
metab_4	0.55	-1.33	-0.13	-0.62	-0.80	-0.46	0.49	0.12	-0.76	-0.07
metab_5	7.05	6.89	7.10	7.01	6.90	6.94	6.77	6.62	6.85	7.24
metab_6	5.79	5.81	5.86	5.95	5.95	5.42	5.82	5.65	5.44	5.60
metab_7	3.75	4.26	4.35	4.24	4.88	4.70	4.08	4.73	3.98	4.30
metab_8	5.07	5.08	5.92	5.41	5.39	4.62	5.10	5.28	4.51	5.45
metab_9	-1.87	-2.30	-1.97	-1.89	-1.55	-1.78	-2.29	-1.64	-2.02	-1.68
metab_10	-2.77	-3.42	-3.40	-2.84	-2.45	-3.14	-3.36	-2.88	-3.05	-2.92